Autism is not a single disorder but comprises a spectrum of disorders. A person with autism can show a range of developmental delays ranging from limited and repetitive behaviours to developing strong interests in some field but being socially impaired. For this reason, autism is also called autism spectrum disorder. No two autistic children are the same. So instead of just predicting if a child has autism or not, a better strategy is to predict the severity of autism.

Centres for Disease Control and Prevention (CDC), in their study involving eight-year-old children from 11 sites in the United States in the year 2014, found out that autism is prevalent in 1 out of every 59 children [1]. This prevalence rate is alarmingly high. And brings upon the need to find the aetiology of autism. It is a tough job to get autistic children into the fMRI scanner let alone do a task-based fMRI study on them. That is why another paradigm called resting state paradigm is quite famous among researchers working on autism. In this paradigm, the subject is asked to lie down in the scanner and asked not to think about anything in particular. Some studies instruct the subjects to close their eyes and some to keep the eyes open and focus on a cross. Because of low compliance benefits, resting state paradigm is heavily used with diseased as well as young population.

Autism related brain alterations are not localized to a specific brain region but it is an impairment of larger brain networks. Researchers have utilized the resting state scans to analyze the alterations in synchrony of brain activity among multiple regions and networks. One measure of this synchrony is functional connectivity. FC is the temporal correlation between two spatially disjoint regions of the brain. Mathematically, it can be represented by Pearson correlation between two time series representing two regions of the brain These two time courses can belong to two different voxels or could even be the mean time courses of two distinct regions. The human brain can be perceived as a graph and the difference in connectivity strengths between these links can be used to explain the severity of autism. Presently, the severity of autism is quantified by various assessments which use questionnaires to measure autism traits and behaviours. Some of these questionnaires are Autism Diagnostic Observation Schedule (ADOS) [3], Autism Diagnostic Interview-Revised (ADI-R) [4], and Social Responsiveness Scale (SRS) [2]. The current study discusses about using fMRI brain scans to quantify the severity of autism.

This study utilized the Autism Brain Imaging Data Exchange (ABIDE) - I dataset [5]. This dataset provides a collection of structural and resting state functional scans from 17 different sites that includes a total of 539 individuals with autism spectrum disorder and 573 typically developing individuals. Details such as affiliations, sample size, diagnostics, scan procedure and parameters pertaining to each of the sites in ABIDE 1 can be found at ABIDE’s website [8].

The inclusion criteria of the subjects for the following study was (i) Males participants, (ii) Age less than or equal to 18 years, ( subjects older than 18 years were not included to minimize the effects of age variation) (iii) Participants were instructed to keep their eyes open during the scan. This study has focused on participants who were diagnosed with autism according to the Diagnostics and Statistical Manual - Version 4 (DSM-IV) criteria. As the data was acquired from multiple sites, to control for heterogeneity, the subjects were matched across autistic and typically developing (TD) groups for age and repetition time (TR). The final dataset consisted of 116 autistic and 156 typically developing subjects with their scans obtained within a TR range of 1500 ms to 2500 ms.

As a part of pre-processing and de-noising the dataset, slice timing correction [9], motion correction[9], bandpass filtering (0.01 Hz - 0.1 Hz) [10], spatial smoothing (6 FWHM Gaussian filter) [9] and removal of the effects of 6 motion parameters was done. The number of volumes was restricted to 120 to match across sites and the first 4 volumes were removed for the MR signal to reach steady state.

Brainnetome brain atlas [6] was employed to extract the mean time courses of 21 bilateral regions (total 42) of DMN. Pearson correlation coefficient of each of these regions with all the voxels of the brain was calculated. The resultant correlation maps for each subject were transformed to standard MNI152 3mm space to create a correlation (i.e. functional connectivity) map for every subject. On the correlation maps resulting from each DMN region, a two sample t-test was performed to compute the group differences which were corrected for multiple comparisons at a false discovery rate (FDR) of p <0.05. Only clusters with more than 10 voxels were considered significant and were presented after mapping to the Brainnetome atlas and probabilistic cerebellar atlas [7].

[1] Baio, J., Wiggins, L., Christensen, D. L., Maenner, M. J., Daniels, J., Warren, Z., Kurzius Spencer, M., Zahorodny, W., Rosenberg, C. R., White, T., et al. (2018). Prevalence of autism spectrum disorder among children aged 8 years—autism and developmental disabilities monitoring network, 11 sites, united states, 2014. MMWR Surveillance Summaries, 67(6):1.

[2] Constantino J.N. (2002). The social responsiveness scale. Los Angeles, CA: Western Psychological Services

[3] Lord, C., Risi, S., Lambrecht, L., Cook, E. H., Leventhal, B. L., DiLavore, P. C.,& Rutter, M. (2000). The Autism Diagnostic Observation Schedule—Generic: A standard measure of social and communication deficits associated with the spectrum of autism. Journal of autism and developmental disorders, 30(3), 205-223.

[4] Lord, C., Rutter, M., & Le Couteur, A. (1994). Autism Diagnostic Interview-Revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. Journal of autism and developmental disorders, 24(5), 659-685.

[5] Di Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., & Deen, B. (2014). The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular psychiatry, 19(6), 659.

[6] Fan, L., Li, H., Zhuo, J., Zhang, Y., Wang, J., Chen, L., & Fox, P. T. (2016). The human brainnetome atlas: a new brain atlas based on connectional architecture. Cerebral cortex, 26(8), 3508-3526.1

[7] Diedrichsen J., Balster J.H., Flavell J., Cussans E., Ramnani N. (2009). A probabilistic MR atlas of the human cerebellum. Neuroimage

[8] Brett Lullo, Adriana Di Martino. ABIDE. http://fcon_1000.projects.nitrc.org/indi/abide/abide_I.html. Accessed On March, 2018

[9] FEAT/User Guide: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FEAT/UserGuide. Accessed on April, 2018

[10] AFNI program: 3dBandpass. https://afni.nimh.nih.gov/pub/dist/doc/program_help/3dBandpass.html. Accessed On April, 2018

[11] Brainnetome Atlas - BNA_subregions.xlsx : http://atlas.brainnetome.org/download.html. Accessed on April 2018

This work can be significant to the industry working in the area of medical diagnosis of psychiatric disorders. One of the major challenges in developing predictive machine learning models is extracting meaningful features from raw data. As the raw data contains a lot of noise and redundant features, feature engineering plays a critical role in the performance of a predictive model. Moreover, any model will be acceptable to doctors only when it is interpretable. The method described in this study can help in solving the problem of interpretable feature selection. The methods developed can be used to develop applications that can assist doctors for early diagnosis of autism as well as keep track of the course of the medical condition.

As this is an ongoing research project. It is not ready to be termed as a technology for diagnosis of autism. Therefore the technology readiness level of this work is intermediate.

The MRI scan of the head (of 3mm³ voxel size) contains ~ 50,000 brain voxels. In the current study, the functional connectivity matrix is of size: Number of DMN regions x Number of voxels creating around 210,00,00 connections. Each of these connections can be used as a features after modelling the connectivity via functional connectivity metric. The major problem in building a machine learning model that can predict the autism severity scores is the sheer number of dimensions. So feature selection plays an essential part. The method described above is a way to obtain interpretable features. These features represent the pairs of regions which differ significantly in terms of FC between the autistic group and TD group which is a very small subset of the total links/features possible. This method of dimensionality reduction of features lets us focus on the relevant links that are actually different across the two groups and can help create a machine learning model for predicting various scores related to autism. This model can be used in early detection of autism as well as in predicting the likely course of the treatment.

The DMN consists of the posterior cingulate cortex (PCC), medial prefrontal cortex (mPFC), precuneus (Prec) and inferior parietal lobule (IPL). After the statistical analysis, the altered connectivity of the DMN regions with the whole brain is shown in Figure 1. The inner 4 clusters of sub-regions represent the 4 regions of DMN and the outer ring of regions represent the rest of the brain. A majority of under-connectivity was observed. That is, the strength of the connectivity links in autistic subjects was significantly less as compared to the strength of connectivity links of TD subjects. See Figure 1. Only 3 instances of over-connectivity were noticed. This was between the middle frontal gyrus and mPFC and between PCC and both insular lobes and dorsal caudate of basal ganglia. See Figure 1. All the regions of the DMN (either left or right or both) except medial area 8 of the superior frontal gyrus were observed to be differently connected to other brain regions. A majority (18.5%) of under-connectivity links of total under-connectivity links were seen to be between Temporal and parietal lobes. See Table 1. Precuneus in the parietal lobe seems to be the reason for the above majority. See Figure 2.

Autism is a neurodevelopmental disorder that characterises an acute impairment of social as well as emotional abilities. An early detection can help to provide better therapies for autistic children. The following study demonstrates an approach for feature selection that can later be used to build machine learning models to predict autism. We define features to represent differences in connectivity between brain regions in autistic as compared to typically developing (TD) individuals. The connectivity metric employed in this study is called functional connectivity (FC). FC between two regions is defined as the Pearson correlation coefficient of time series of those two regions. We used resting-state functional magnetic resonance imaging (fMRI) brain scans to get the time series for each region. This study dives into finding the region pairs (or links between 2 regions) whose functional connectivity is altered in autistic brains as compared to age-matched typically developing individuals. A functional connectivity matrix for each subject’s brain scan was created. A two sample t-test was performed across the two groups' subjects' matrices. The two groups consisted of 116 autistic and 156 typically developing subjects. A widespread under-connectivity i.e. the strength of the functional connectivity links in autistic group significantly less than the strength of functional connectivity links of TD group. Over-connectivity of connectivity links of autistic subjects was also observed in autistic group but was limited to only a few regions. We found the majority of significantly under-connected links between temporal and parietal lobes of brain.

Figure 1: Blue edges represent under-connectivity (i.e. Autistic < TD) and red edges represents over-connectivity. Node color and size signifies the degree of node [1-32] with 1 denoted by blue (smallest node) and 32 denoted by orange (largest node). The nodes inside the circle are regions of DMN. For region names, refer to brainnetome atlas region description file [11].

Table 1: Table showing percentage of under-connected links between two lobes out of total under-connected links

Figure 2: Chart showing number of brain regions and lobes differently connected via links of the DMN regions